Conformal Predictions Enhanced Expert-guided Meshing with Graph Neural Networks

TL;DR

This paper introduces a machine learning approach using Graph Neural Networks and conformal predictions to automate CFD mesh generation for aircraft models, achieving expert-level quality and significantly reducing computation time.

Contribution

It presents a novel GNN-based mesh segmentation method combined with conformal prediction for uncertainty quantification, improving automation and robustness in CFD meshing.

Findings

Outperforms state-of-the-art segmentation models PointNet++ and PointMLP.

Provides statistical guarantees with conformal predictions to prevent under-refinement.

Achieves a 5x speedup compared to adaptive remeshing in a real-world case study.

Abstract

Computational Fluid Dynamics (CFD) is widely used in different engineering fields, but accurate simulations are dependent upon proper meshing of the simulation domain. While highly refined meshes may ensure precision, they come with high computational costs. Similarly, adaptive remeshing techniques require multiple simulations and come at a great computational cost. This means that the meshing process is reliant upon expert knowledge and years of experience. Automating mesh generation can save significant time and effort and lead to a faster and more efficient design process. This paper presents a machine learning-based scheme that utilizes Graph Neural Networks (GNN) and expert guidance to automatically generate CFD meshes for aircraft models. In this work, we introduce a new 3D segmentation algorithm that outperforms two state-of-the-art models, PointNet++ and PointMLP, for surface classification. We also present a novel approach to project predictions from 3D mesh segmentation models to CAD surfaces using the conformal predictions method, which provides marginal statistical guarantees and robust uncertainty quantification and handling. We demonstrate that the addition of conformal predictions effectively enables the model to avoid under-refinement, hence failure, in CFD meshing even for weak and less accurate models. Finally, we demonstrate the efficacy of our approach through a real-world case study that demonstrates that our automatically generated mesh is comparable in quality to expert-generated meshes and enables the solver to converge and produce accurate results. Furthermore, we compare our approach to the alternative of adaptive remeshing in the same case study and find that our method is 5 times faster in the overall process of simulation. The code and data for this project are made publicly available at https://github.com/ahnobari/AutoSurf.